Neuromorphic computing has gained great attention as the traditional Boolean computing based on CMOS technology is reaching its physical limits. Inspired by the computational capability of the human brain, cognitive computing and learning has become an increasingly attractive paradigm for future computation beyond the von Neumann architecture. Recent advances in neuro-inspired machine learning algorithms have shown tremendous success in speech/image recognition.
To implement large scale neuromorphic computing, large resistive networks of resistive devices are provided where the resistive devices have conductances that can be provided in multiple conductance states. Building these resistive networks with emerging non-volatile resistive devices is attractive as these non-volatile resistive devices tend to be more compact and less costly. However, current neuromorphic computational systems assume that the conductances of the non-volatile resistive devices can be changed linearly using identical voltage pulses. For many applications, this assumption is not justified and can result in unacceptably computational inaccuracy. One source of non-linearity is that an off conductance state of the resistive devices is not zero. Ideally, an on conductance state to off conductance state ratio (ON/OFF ratio) is infinite and in practice can be assumed to be infinite if the ON/OFF ratio is sufficiently high. Unfortunately, resistive devices typically have ON/OFF ratios of between 15 and 40 depending on the type of resistive devices being utilized in the resistive network. Thus, while current neuromorphic computational systems assume that the ON/OF conductance ratio is infinite, an ON/OFF ratio of between 15 and 40 is not sufficient to allow the neuromorphic computational systems to operate under this assumption because the non-linearity leads to unacceptably high computational errors. Therefore, new techniques are needed that can ameliorate the effect of finite ON/OFF ratios and thereby provide better computational accuracy in a neuromorphic computational system.